forked from PulseFocusPlatform/PulseFocusPlatform
66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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import numpy as np
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import datetime
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__all__ = ['TrainingStats', 'Time']
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class SmoothedValue(object):
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"""Track a series of values and provide access to smoothed values over a
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window or the global series average.
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"""
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def __init__(self, window_size):
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self.deque = collections.deque(maxlen=window_size)
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def add_value(self, value):
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self.deque.append(value)
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def get_median_value(self):
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return np.median(self.deque)
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def Time():
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return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
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class TrainingStats(object):
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def __init__(self, window_size, stats_keys):
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self.smoothed_losses_and_metrics = {
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key: SmoothedValue(window_size)
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for key in stats_keys
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}
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def update(self, stats):
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for k, v in self.smoothed_losses_and_metrics.items():
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v.add_value(stats[k])
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def get(self, extras=None):
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stats = collections.OrderedDict()
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if extras:
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for k, v in extras.items():
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stats[k] = v
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for k, v in self.smoothed_losses_and_metrics.items():
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stats[k] = format(v.get_median_value(), '.6f')
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return stats
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def log(self, extras=None):
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d = self.get(extras)
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strs = ', '.join(str(dict({x: y})).strip('{}') for x, y in d.items())
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return strs
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